Data is from August, 2017 sequencing. This is week 4 of experiment 1

Download and map:

Working on Whitney for read mapping. Directory: /Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/jmaloof/2017/Wyoming-microbiome/20170830-data/

20170830 reads

Build index (not done…already exists)

cd ~/Sequences/ref_genomes/B_rapa/genome/V3.0

wget http://brassicadb.org/brad/datasets/pub/BrassicaceaeGenome/Brassica_rapa/V3.0/Brapa_genome_v3.0_cds.fasta.gz 

kallisto index -i B_rapa_CDS_V3.0_k31_kallisto_index Brapa_genome_v3.0_cds.fasta.gz

cd ~/2017/Wyoming-microbiome//20170830-samples/20170830-data

Map reads

mkdir kallisto_out_V3.0

#actually a fish loop
for file in (ls raw-fastq/2017-08-27/*.fastq.gz)
  echo $file
  set newfile (basename $file _R1_001.fastq.gz)
  kallisto quant -i ~/Sequences/ref_genomes/B_rapa/genome/V3.0/B_rapa_CDS_V3.0_k31_kallisto_index  -o kallisto_out_V3.0/$newfile --single -l 200 -s 40 -t 4 --plaintext $file
end

Move the counts to my local computer

NEED TO MODIFY BELOW

cd /Users/jmaloof/git/Br_Microbe_Paper_2021/RNA/input/20170830-samples
lftp sftp://whitney.plb.ucdavis.edu
  cd 2017/Wyoming-microbiome/20170830-samples/20170830-data
  mirror kallisto_out_V3.0

remove unused files

cd /Users/jmaloof/git/Br_Microbe_Paper_2021/RNA/input/20170830-samples/kallisto_out_V3.0
rm */*.json

compress tsv files

cd /Users/jmaloof/git/Br_Microbe_Paper_2021/RNA/input/20170830-samples/kallisto_out_V3.0
gzip */abundance.tsv

Get counts into R

library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.1.2     ✓ dplyr   1.0.6
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(stringr)
library(edgeR)
Loading required package: limma
head(kallisto_names)
[1] "wyo_leaf_R500_10_417_S65_L001" "wyo_leaf_R500_10_417_S65_L002"
[3] "wyo_leaf_R500_10_417_S65_L003" "wyo_leaf_R500_10_417_S65_L004"
[5] "wyo_leaf_R500_10_419_S66_L001" "wyo_leaf_R500_10_419_S66_L002"
counts <- tibble(sample = kallisto_names, file = kallisto_files) %>%
  mutate(countdata = map(kallisto_files, read_tsv))

── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)


── Column specification ─────────────────────────────────────────────────
cols(
  target_id = col_character(),
  length = col_double(),
  eff_length = col_double(),
  est_counts = col_double(),
  tpm = col_double()
)
head(counts)

reformat into rows = genes and columns = samples

counts <- counts %>% unnest(countdata) %>%
  mutate(sample = str_remove(sample, "_L.*")) %>% 
  select(sample, target_id, est_counts) %>%
  group_by(sample, target_id) %>%
  summarize(est_counts=sum(est_counts)) %>% # sum up counts from multiple lanes
  ungroup() %>%
  pivot_wider(id_cols = target_id,
              names_from = sample,
              values_from = est_counts)
`summarise()` has grouped output by 'sample'. You can override using the `.groups` argument.
head(counts)
dim(counts)
[1] 46250    25
write_csv(counts,"../../output/20170830_V3.0_raw_counts_.csv.gz")

make sample description data frame

sample.description <- tibble(sample=colnames(counts)[-1]) %>%
  separate(sample,
           c("location","tissue","genotype","block","pot"),
           remove=FALSE,
           convert=TRUE) 
head(sample.description)

## get additional metadata
sample.info <- readxl::read_excel("../input/wy001_RNAseq.xlsx",sheet = 1)
head(sample.info)

##combine
sample.description <- left_join(sample.description, sample.info)
sample.description <- sample.description %>% 
  mutate(group=paste(tissue,genotype,soil,autoclave,sep="_"))
head(sample.description)
sample.description %>% summarize(n_distinct(group))

summarize counts

pl.orig <- counts[,-1] %>% colSums() %>% tibble(sample=names(.),count=.) %>%
  ggplot(aes(x=sample,y=count)) + 
  geom_col() +
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
pl.orig

load to edgeR, normalize

#confirm that everthing is in the right order
all(colnames(counts)[-1]==sample.description$sample)
dge <- DGEList(counts[,-1],
               group=sample.description$group,
               samples=sample.description,
               genes=counts$target_id)
dge <- calcNormFactors(dge)
barplot(dge$samples$lib.size)
ggplot(dge$samples,aes(x=sample,y=norm.factors,fill=tissue)) + geom_col() + 
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
ggplot(dge$samples,aes(x=sample,y=norm.factors,fill=genotype)) + geom_col() + 
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
ggplot(dge$samples,aes(x=sample,y=norm.factors,fill=as.factor(block))) + geom_col() +
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 

Looks like we should normalize separately for root and leaf

do separately for leaf and root values

counts.leaf <- counts %>% select(target_id, contains("leaf"))
counts.root <- counts %>% select(target_id, contains("root"))
sample.description.leaf <- sample.description %>% filter(tissue=="leaf")
sample.description.root <- sample.description %>% filter(tissue=="root")

Leaf

#confirm that everthing is in the right order
all(colnames(counts.leaf)[-1]==sample.description.leaf$sample)
dge.leaf <- DGEList(counts.leaf[,-1],
                    group=sample.description.leaf$group,
                    samples=sample.description.leaf,
                    genes=counts.leaf$target_id)
dge.leaf <- calcNormFactors(dge.leaf)

Root

#confirm that everthing is in the right order
all(colnames(counts.root)[-1]==sample.description.root$sample)
dge.root <- DGEList(counts.root[,-1],
                    group=sample.description.root$group,
                    samples=sample.description.root,
                    genes=counts.root$target_id)
dge.root <- calcNormFactors(dge.root)
save(dge.leaf,dge.root,sample.description.leaf,sample.description.root,file="../output/edgeR_dge_objects.Rdata")

Write out cpm values

cpm.leaf.w <- bind_cols(dge.leaf$gene,as_tibble(cpm(dge.leaf))) %>% as_tibble() %>% rename(transcript_ID=genes)
head(cpm.leaf.w)
write_csv(cpm.leaf.w,"../output/cpm_wide_20170617_leaf_samples.csv.gz")
cpm.root.w <- bind_cols(dge.root$gene,as_tibble(cpm(dge.root))) %>% as_tibble() %>% rename(transcript_ID=genes)
head(cpm.root.w)
write_csv(cpm.root.w,"../output/cpm_wide_20170617_root_samples.csv.gz")

Also let’s reformat this to long format and add metadata

cpm.leaf.long <- cpm.leaf.w %>% 
  gather(-transcript_ID,key = sample,value=cpm) %>%
  left_join(sample.description.leaf)
head(cpm.leaf.long)
write_csv(cpm.leaf.long,"../output/cpm_long_with_metadata_20170617_leaf_samples.csv.gz")
cpm.root.long <- cpm.root.w %>% 
  gather(-transcript_ID,key = sample,value=cpm) %>%
  left_join(sample.description.root)
head(cpm.root.long)
write_csv(cpm.root.long,"../output/cpm_long_with_metadata_20170617_root_samples.csv.gz")

compute and write out voom-transformed values

design.leaf <- model.matrix(~ sample.description.leaf$group)
dge4voom.leaf <- dge.leaf[rowSums(cpm(dge.leaf)>1) >= 6,,keep.lib.sizes=FALSE]
dge4voom.leaf <- calcNormFactors(dge4voom.leaf)
data.voom.leaf <- voom(dge4voom.leaf,design = design.leaf)
data.voom.exp.leaf <- bind_cols(data.voom.leaf$genes,as_tibble(data.voom.leaf$E)) %>%
  rename(transcript_ID=genes) %>% as_tibble()
head(data.voom.exp.leaf)
write_csv(data.voom.exp.leaf, "../output/voom_expression_20170617_T6_leaf_samples.csv.gz")
voom.long.leaf <- data.voom.exp.leaf %>% 
  gather(-transcript_ID,key = sample,value=expression) %>%
  left_join(sample.description.leaf)
head(voom.long.leaf)
hist(voom.long.leaf$expression)
write_csv(voom.long.leaf,"../output/voom_long_with_metadata_20170617_T6_leaf_samples.csv.gz")
design.root <- model.matrix(~ sample.description.root$group)
dge4voom.root <- dge.root[rowSums(cpm(dge.root)>1) >= 6,,keep.lib.sizes=FALSE]
dge4voom.root <- calcNormFactors(dge4voom.root)
data.voom.root <- voom(dge4voom.root,design = design.root)
data.voom.exp.root <- bind_cols(data.voom.root$genes,as_tibble(data.voom.root$E)) %>%
  rename(transcript_ID=genes) %>% as_tibble()
head(data.voom.exp.root)
write_csv(data.voom.exp.root, "../output/voom_expression_20170617_T6_root_samples.csv.gz")
voom.long.root <- data.voom.exp.root %>% 
  gather(-transcript_ID,key = sample,value=expression) %>%
  left_join(sample.description.root)
head(voom.long.root)
hist(voom.long.root$expression)
write_csv(voom.long.root,"../output/voom_long_with_metadata_20170617_T6_root_samples.csv.gz")

write it to irods

Need to run this yourself in terminal

iinit
icd /iplant/home/shared/ucd.brassica/analyses/Brapa_Microbiome_RNAseq/
for f in (ls cpm*)
    echo $f
    iput -vf $f
end
for f in (ls voom*)
    echo $f
    iput -vf $f
end

read distribution

Mike asked if the difference in normalization factors in leafs vs roots was due to high abundance of photosynthesis transcripts in leafs. (Although leafs have lower normalization factor)

Start by doing a simplistic normalization just by library size. Then look at distribution of most abundant counts.

counts.leaf.norm <- counts.leaf %>% 
  mutate_at(-1,funs(./sum(.))) %>% 
  transmute(target_id=target_id,mean= {select(.,-target_id) %>% rowMeans()}) %>%
  arrange(desc(mean)) %>% mutate(cumsum=cumsum(mean),rank=row_number(),sample="leaf")
counts.leaf.norm
counts.root.norm <- counts.root %>% 
  mutate_at(-1,funs(./sum(.))) %>% 
  transmute(target_id=target_id,mean= {select(.,-target_id) %>% rowMeans()}) %>%
  arrange(desc(mean)) %>% mutate(cumsum=cumsum(mean),rank=row_number(),sample="root")
counts.root.norm
rbind(counts.leaf.norm,counts.root.norm) %>%
  ggplot(aes(x=rank,y=cumsum,color=sample)) +
  geom_line() +xlim(0,20000)
rbind(counts.leaf.norm,counts.root.norm) %>%
  ggplot(aes(x=rank,y=mean,color=sample)) +
  geom_line()  + scale_y_log10()
rbind(counts.leaf.norm,counts.root.norm) %>% filter(rank < 41) %>%
  ggplot(aes(x=rank,y=mean,fill=sample)) +
  geom_col(position = "dodge") 
annotation <- read_csv("../../../Annotation/output/v3.0annotation/Brapa_V3.0_annotated.csv")
top.expressed.leaf <- counts.leaf.norm %>% filter(rank<21) %>% left_join(annotation,by=c("target_id"="name")) %>% select("target_id","mean","rank","AGI","At_symbol","At_description") %>% arrange(rank)
top.expressed.leaf
write_csv(top.expressed.leaf,"../output/top.expressed.leaf.csv")
top.expressed.root <- counts.root.norm %>% filter(rank<21) %>% left_join(annotation,by=c("target_id"="name")) %>% select("target_id","mean","rank","AGI","At_symbol","At_description") %>% arrange(rank)
top.expressed.root
write_csv(top.expressed.root,"../output/top.expressed.root.csv")
---
title: "RNA expression analysis of Brassica Microbe Data. I: prep data"
output: html_notebook
---

Data is from August, 2017 sequencing.  This is week 4 of experiment 1

## Download and map:

Working on Whitney for read mapping.  Directory: `/Network/Servers/avalanche.plb.ucdavis.edu/Volumes/Mammoth/Users/jmaloof/2017/Wyoming-microbiome/20170830-data/`

### 20170830 reads

Build index (not done...already exists)
```{r, engine='bash', eval=FALSE}
cd ~/Sequences/ref_genomes/B_rapa/genome/V3.0

wget http://brassicadb.org/brad/datasets/pub/BrassicaceaeGenome/Brassica_rapa/V3.0/Brapa_genome_v3.0_cds.fasta.gz 

kallisto index -i B_rapa_CDS_V3.0_k31_kallisto_index Brapa_genome_v3.0_cds.fasta.gz

cd ~/2017/Wyoming-microbiome//20170830-samples/20170830-data
```

Map reads

```{r, engine='bash',eval=FALSE}
mkdir kallisto_out_V3.0

#actually a fish loop
for file in (ls raw-fastq/2017-08-27/*.fastq.gz)
  echo $file
  set newfile (basename $file _R1_001.fastq.gz)
  kallisto quant -i ~/Sequences/ref_genomes/B_rapa/genome/V3.0/B_rapa_CDS_V3.0_k31_kallisto_index  -o kallisto_out_V3.0/$newfile --single -l 200 -s 40 -t 4 --plaintext $file
end
```

Move the counts to my local computer

### NEED TO MODIFY BELOW

```{r, engine='bash', eval=FALSE}
cd /Users/jmaloof/git/Br_Microbe_Paper_2021/RNA/input/20170830-samples
lftp sftp://whitney.plb.ucdavis.edu
  cd 2017/Wyoming-microbiome/20170830-samples/20170830-data
  mirror kallisto_out_V3.0
```

remove unused files
```{r, engine='bash', eval=FALSE}
cd /Users/jmaloof/git/Br_Microbe_Paper_2021/RNA/input/20170830-samples/kallisto_out_V3.0
rm */*.json
```

compress tsv files
```{r, engine='bash', eval=FALSE}
cd /Users/jmaloof/git/Br_Microbe_Paper_2021/RNA/input/20170830-samples/kallisto_out_V3.0
gzip */abundance.tsv
```


## Get counts into R

```{r}
library(tidyverse)
library(stringr)
library(edgeR)
```



```{r}
kallisto_files <- dir(path = "../../input/20170830-samples/kallisto_out_V3.0",pattern="abundance.tsv",recursive = TRUE,full.names = TRUE)
kallisto_names <- str_split(kallisto_files,"/",simplify=TRUE)[,6]
head(kallisto_names)
```

```{r}
counts <- tibble(sample = kallisto_names, file = kallisto_files) %>%
  mutate(countdata = map(kallisto_files, read_tsv)) %>%
  select(-file)

head(counts)
```

reformat into rows = genes and columns = samples
```{r}
counts <- counts %>% unnest(countdata) %>%
  mutate(sample = str_remove(sample, "_L.*")) %>% 
  select(sample, target_id, est_counts) %>%
  group_by(sample, target_id) %>%
  summarize(est_counts=sum(est_counts)) %>% # sum up counts from multiple lanes
  ungroup() %>%
  pivot_wider(id_cols = target_id,
              names_from = sample,
              values_from = est_counts)
```

```{r}
head(counts)
dim(counts)
```

```{r}
write_csv(counts,"../../output/20170830_V3.0_raw_counts_.csv.gz")
```


## make sample description data frame


```{r}
sample.description <- tibble(sample=colnames(counts)[-1]) %>%
  separate(sample,
           c("location","tissue","genotype","block","pot"),
           remove=FALSE,
           convert=TRUE) 
head(sample.description)

## get additional metadata
sample.info <- readxl::read_excel("../input/wy001_RNAseq.xlsx",sheet = 1)
head(sample.info)

##combine
sample.description <- left_join(sample.description, sample.info)
sample.description <- sample.description %>% 
  mutate(group=paste(tissue,genotype,soil,autoclave,sep="_"))
head(sample.description)
sample.description %>% summarize(n_distinct(group))
```

## summarize counts
```{r}
pl.orig <- counts[,-1] %>% colSums() %>% tibble(sample=names(.),count=.) %>%
  ggplot(aes(x=sample,y=count)) + 
  geom_col() +
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
pl.orig
```

## load to edgeR, normalize

```{r}
#confirm that everthing is in the right order
all(colnames(counts)[-1]==sample.description$sample)
dge <- DGEList(counts[,-1],
               group=sample.description$group,
               samples=sample.description,
               genes=counts$target_id)
```

```{r}
dge <- calcNormFactors(dge)
barplot(dge$samples$lib.size)
ggplot(dge$samples,aes(x=sample,y=norm.factors,fill=tissue)) + geom_col() + 
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
ggplot(dge$samples,aes(x=sample,y=norm.factors,fill=genotype)) + geom_col() + 
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
ggplot(dge$samples,aes(x=sample,y=norm.factors,fill=as.factor(block))) + geom_col() +
  theme(axis.text.x  = element_text(angle=90, vjust=0.5,size = 7)) 
```

Looks like we should normalize separately for root and leaf

# do separately for leaf and root values

```{r}
counts.leaf <- counts %>% select(target_id, contains("leaf"))
counts.root <- counts %>% select(target_id, contains("root"))
sample.description.leaf <- sample.description %>% filter(tissue=="leaf")
sample.description.root <- sample.description %>% filter(tissue=="root")
```

Leaf
```{r}
#confirm that everthing is in the right order
all(colnames(counts.leaf)[-1]==sample.description.leaf$sample)
dge.leaf <- DGEList(counts.leaf[,-1],
                    group=sample.description.leaf$group,
                    samples=sample.description.leaf,
                    genes=counts.leaf$target_id)
dge.leaf <- calcNormFactors(dge.leaf)
```

Root
```{r}
#confirm that everthing is in the right order
all(colnames(counts.root)[-1]==sample.description.root$sample)
dge.root <- DGEList(counts.root[,-1],
                    group=sample.description.root$group,
                    samples=sample.description.root,
                    genes=counts.root$target_id)
dge.root <- calcNormFactors(dge.root)
```

```{r}
save(dge.leaf,dge.root,sample.description.leaf,sample.description.root,file="../output/edgeR_dge_objects.Rdata")
```


## Write out cpm values

```{r}
cpm.leaf.w <- bind_cols(dge.leaf$gene,as_tibble(cpm(dge.leaf))) %>% as_tibble() %>% rename(transcript_ID=genes)
head(cpm.leaf.w)
write_csv(cpm.leaf.w,"../output/cpm_wide_20170617_leaf_samples.csv.gz")
```

```{r}
cpm.root.w <- bind_cols(dge.root$gene,as_tibble(cpm(dge.root))) %>% as_tibble() %>% rename(transcript_ID=genes)
head(cpm.root.w)
write_csv(cpm.root.w,"../output/cpm_wide_20170617_root_samples.csv.gz")
```


Also let's reformat this to long format and add metadata

```{r}
cpm.leaf.long <- cpm.leaf.w %>% 
  gather(-transcript_ID,key = sample,value=cpm) %>%
  left_join(sample.description.leaf)
head(cpm.leaf.long)
write_csv(cpm.leaf.long,"../output/cpm_long_with_metadata_20170617_leaf_samples.csv.gz")
```

```{r}
cpm.root.long <- cpm.root.w %>% 
  gather(-transcript_ID,key = sample,value=cpm) %>%
  left_join(sample.description.root)
head(cpm.root.long)
write_csv(cpm.root.long,"../output/cpm_long_with_metadata_20170617_root_samples.csv.gz")
```


## compute and write out voom-transformed values

```{r}
design.leaf <- model.matrix(~ sample.description.leaf$group)
dge4voom.leaf <- dge.leaf[rowSums(cpm(dge.leaf)>1) >= 6,,keep.lib.sizes=FALSE]
dge4voom.leaf <- calcNormFactors(dge4voom.leaf)
data.voom.leaf <- voom(dge4voom.leaf,design = design.leaf)
data.voom.exp.leaf <- bind_cols(data.voom.leaf$genes,as_tibble(data.voom.leaf$E)) %>%
  rename(transcript_ID=genes) %>% as_tibble()
head(data.voom.exp.leaf)
write_csv(data.voom.exp.leaf, "../output/voom_expression_20170617_T6_leaf_samples.csv.gz")
```


```{r}
voom.long.leaf <- data.voom.exp.leaf %>% 
  gather(-transcript_ID,key = sample,value=expression) %>%
  left_join(sample.description.leaf)
head(voom.long.leaf)
hist(voom.long.leaf$expression)
write_csv(voom.long.leaf,"../output/voom_long_with_metadata_20170617_T6_leaf_samples.csv.gz")
```


```{r}
design.root <- model.matrix(~ sample.description.root$group)
dge4voom.root <- dge.root[rowSums(cpm(dge.root)>1) >= 6,,keep.lib.sizes=FALSE]
dge4voom.root <- calcNormFactors(dge4voom.root)
data.voom.root <- voom(dge4voom.root,design = design.root)
data.voom.exp.root <- bind_cols(data.voom.root$genes,as_tibble(data.voom.root$E)) %>%
  rename(transcript_ID=genes) %>% as_tibble()
head(data.voom.exp.root)
write_csv(data.voom.exp.root, "../output/voom_expression_20170617_T6_root_samples.csv.gz")
```


```{r}
voom.long.root <- data.voom.exp.root %>% 
  gather(-transcript_ID,key = sample,value=expression) %>%
  left_join(sample.description.root)
head(voom.long.root)
hist(voom.long.root$expression)
write_csv(voom.long.root,"../output/voom_long_with_metadata_20170617_T6_root_samples.csv.gz")
```

write it to irods

Need to run this yourself in terminal

```{r, engine='bash', eval=FALSE}
iinit
icd /iplant/home/shared/ucd.brassica/analyses/Brapa_Microbiome_RNAseq/
for f in (ls cpm*)
    echo $f
    iput -vf $f
end
for f in (ls voom*)
    echo $f
    iput -vf $f
end
```

## read distribution

Mike asked if the difference in normalization factors in leafs vs roots was due to high abundance of photosynthesis transcripts in leafs.  (Although leafs have lower normalization factor)

Start by doing a simplistic normalization just by library size.  Then look at distribution of most abundant counts.

```{r}
counts.leaf.norm <- counts.leaf %>% 
  mutate_at(-1,funs(./sum(.))) %>% 
  transmute(target_id=target_id,mean= {select(.,-target_id) %>% rowMeans()}) %>%
  arrange(desc(mean)) %>% mutate(cumsum=cumsum(mean),rank=row_number(),sample="leaf")
counts.leaf.norm
```
```{r}
counts.root.norm <- counts.root %>% 
  mutate_at(-1,funs(./sum(.))) %>% 
  transmute(target_id=target_id,mean= {select(.,-target_id) %>% rowMeans()}) %>%
  arrange(desc(mean)) %>% mutate(cumsum=cumsum(mean),rank=row_number(),sample="root")
counts.root.norm
```

```{r}
rbind(counts.leaf.norm,counts.root.norm) %>%
  ggplot(aes(x=rank,y=cumsum,color=sample)) +
  geom_line() +xlim(0,20000)
```

```{r}
rbind(counts.leaf.norm,counts.root.norm) %>%
  ggplot(aes(x=rank,y=mean,color=sample)) +
  geom_line()  + scale_y_log10()
```

```{r}
rbind(counts.leaf.norm,counts.root.norm) %>% filter(rank < 41) %>%
  ggplot(aes(x=rank,y=mean,fill=sample)) +
  geom_col(position = "dodge") 
```

```{r}
annotation <- read_csv("../../../Annotation/output/v3.0annotation/Brapa_V3.0_annotated.csv")
```

```{r}
top.expressed.leaf <- counts.leaf.norm %>% filter(rank<21) %>% left_join(annotation,by=c("target_id"="name")) %>% select("target_id","mean","rank","AGI","At_symbol","At_description") %>% arrange(rank)
top.expressed.leaf
write_csv(top.expressed.leaf,"../output/top.expressed.leaf.csv")
```

```{r}
top.expressed.root <- counts.root.norm %>% filter(rank<21) %>% left_join(annotation,by=c("target_id"="name")) %>% select("target_id","mean","rank","AGI","At_symbol","At_description") %>% arrange(rank)
top.expressed.root
write_csv(top.expressed.root,"../output/top.expressed.root.csv")
```